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	<title>The maths behind ControlPlane</title>
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			<h1>The maths behind ControlPlane</h1>
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		<p style="font-style: italic;">Note: this is fairly technical,
		and is aimed at an undergraduate mathematics level. It is not
		necessary to understand the following content to use ControlPlane;
		it is provided for the curious.
		</p>
		<p>ControlPlane doesn't use traditional "fuzzy logic" handling,
		but rather combines fuzzy sets with a more traditional
		confidence combination method. The fuzzy sets are implicit, and
		represent each possible context: a given rule indicates that a
		piece of evidence is in a certain context set with a certain
		membership value (the confidence).
		</p>
		<p>Every time ControlPlane decides to evaluate the best guess, it
		finds all the rules that match, groups them based on the
		context that they provide evidence for, and then aggregates
		the confidence values for each context. The context with the
		highest aggregated confidence is then taken to be the best
		guess.
		</p>
		<p>Confidence values are assumed to be independent, and are
		aggregated according to the following equation: (a modification
		of Hooper's Rule)
		</p>
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			<img src="../gfx/equation-0.png" alt="Equation 0" />
		</p>
		<p>For example, if two rules match for a specific context, one
		having 50% confidence and the other having 70% confidence:
		</p>
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			<img src="../gfx/equation-1.png" alt="Equation 1" />
		</p>

		<h2 class="seealso">See also</h2>
		<p class="seealso">
			<a href="rules.html">Setting up ControlPlane's rules</a><br />
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